Dataset Information

Screening methods for detection of ancient Mycobacterium tuberculosis complex fingerprints in next-generation sequencing data derived from skeletal samples.

ABSTRACT: BACKGROUND:Recent advances in ancient DNA studies, especially in increasing isolated DNA yields and quality, have opened the possibility of analysis of ancient host microbiome. However, such pitfalls as spurious identification of pathogens based on fragmentary data or environmental contamination could lead to incorrect epidaemiological conclusions. Within the Mycobacterium genus, Mycobacterium tuberculosis complex members responsible for tuberculosis share up to ?99% genomic sequence identity, while other more distantly related Mycobacteria other than M. tuberculosis can be causative agents for pulmonary diseases or soil dwellers. Therefore, reliable determination of species complex is crucial for interpretation of sequencing results. RESULTS:Here we present a novel bioinformatical approach, used for screening of ancient tuberculosis in sequencing data, derived from 28 individuals (dated 4400-4000 and 3100-2900 BC) from central Poland. We demonstrate that cost-effective next-generation screening sequencing data (?20M reads per sample) could yield enough information to provide statistically supported identification of probable ancient disease cases. CONCLUSIONS:Application of appropriate bioinformatic tools, including an unbiased selection of genomic alignment targets for species specificity, makes it possible to extract valid data from full-sample sequencing results (without subjective targeted enrichment procedures). This approach broadens the potential scope of palaeoepidaemiology both to older, suboptimally preserved samples and to pathogens with difficult intrageneric taxonomy.

SUBMITTER: Borowka P

PROVIDER: S-EPMC6586198 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Screening methods for detection of ancient Mycobacterium tuberculosis complex fingerprints in next-generation sequencing data derived from skeletal samples.

Borówka Paulina P Pułaski Łukasz Ł Marciniak Błażej B Borowska-Strugińska Beata B Dziadek Jarosław J Żądzińska Elżbieta E Lorkiewicz Wiesław W Strapagiel Dominik D

GigaScience 20190601 6

<h4>Background</h4>Recent advances in ancient DNA studies, especially in increasing isolated DNA yields and quality, have opened the possibility of analysis of ancient host microbiome. However, such pitfalls as spurious identification of pathogens based on fragmentary data or environmental contamination could lead to incorrect epidaemiological conclusions. Within the Mycobacterium genus, Mycobacterium tuberculosis complex members responsible for tuberculosis share up to ∼99% genomic sequence ide ...[more]

PMID: 31220249

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Project description:Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.

Project description:Background: Tuberculosis (TB) is now the leading cause of death from infectious disease. Rapid screening and diagnostic methods for TB are urgently required. Rapid development of metagenomics next-generation sequencing (mNGS) in recent years showed promising and satisfying application of mNGS in several kinds of infectious diseases. However, research directly evaluating the ability of mNGS in TB infection is still scarce. Methods: We conducted an adult prospective study in mainland China to evaluate the diagnostic performance of mNGS for detection of Mycobacterium tuberculosis complex (MTB) in multiple forms of direct clinical samples compared with GeneXpert MTB/RIF assay (Xpert), traditional diagnostic methods, and the clinical final diagnosis. Results: Of 123 patients presenting with suspected active TB infection between June 1, 2017, and May 21, 2018, 105 patients underwent synchronous tuberculous testing with culture, Xpert, and mNGS on direct clinical samples including sputum, cerebrospinal fluids, pus, etc. During follow-up, 45 of 105 participants had clinical final diagnosis of active TB infection, including 13 pulmonary TB cases and 32 extrapulmonary TB cases. Compared to clinical final diagnosis, mNGS produced a sensitivity of 44% for all active TB cases, which was similar to Xpert (42%) but much higher than conventional methods (29%). With only one false-positive result, mNGS had a specificity of 98% in our study. mNGS yielded significantly much higher sensitivity in pre-treatment samples (76%) than post-treatment ones (31%) (P = 0.005), which was also true for Xpert and conventional methods. Combining Xpert and mNGS together, the study identified 27 of 45 active TB cases (60%), including all 13 conventional method-identified cases, and the result reached statistical significance compared to conventional methods (McNemar-test P < 0.001). Conclusions: mNGS had a similar diagnostic ability of MTB compared with Xpert and showed potential for a variety of clinical samples. Combined mNGS and Xpert showed an overall superior advantage over conventional methods and significantly improved the etiology diagnosis of both MTB and other pathogens. The result that anti-TB treatment significantly reduced diagnostic efficacy of culture, Xpert, and mNGS highlighted the importance of collecting samples before empirical treatment.

Dataset Information

Screening methods for detection of ancient Mycobacterium tuberculosis complex fingerprints in next-generation sequencing data derived from skeletal samples.

Publications

Screening methods for detection of ancient Mycobacterium tuberculosis complex fingerprints in next-generation sequencing data derived from skeletal samples.

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